2021
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat its Cake?
ICASSP 2021accepted
Active learning is the set of techniques for intelligently labeling large unlabeled datasets to reduce the labeling effort. In parallel, recent developments in self-supervised and semi-supervised learning (S4L) provide powerful techniques, based on data-augmentation, contrastive learning, and self-t…